Abstract: Application of health risk assessment methods is important in order to comprehend the risk of human exposure to heavy metals and other dangerous pollutants. Four soil samples were collected at distances of 10, 20, 30 m and the control 100 m away from the dump site at depths of 0.3, 0.6 and 0.9 m. The collected soil samples were examined for Zn, Cu, Pb, Cd and Ni using standard methods. The health risks via the main pathways of human exposure to heavy metal were detected using relevant standard equations. Hazard quotient was calculated to determine non-carcinogenic health risk for each individual heavy metal. Life time cancer risk was calculated to determine the cumulative life cancer rating for each exposure pathway. The estimated health risk values for adults and children were generally lower than the reference dose. The calculated hazard quotient for the ingestion, inhalation and dermal contact pathways were less than unity. This means that there is no detrimental concern to the health on human exposure to heavy metals in contaminated soil. The life time cancer risk 5.4 × 10-2 was higher than the acceptable threshold value of 1 × 10-4 which is reflected to have significant health effects on human exposure to heavy metals in contaminated soil. Good hygienic practices are recommended to ease the potential risk to children and adult who are exposed to contaminated soils. Also, the local authorities should be made aware of such health risks for the purpose of planning the management strategy accordingly.
Abstract: Vehicular Adhoc Networks (VANETs), a subset of Mobile Adhoc Networks (MANETs), refers to a set of smart vehicles used for road safety. This vehicle provides communication services among one another or with the Road Side Unit (RSU). Security is one of the most critical issues related to VANET as the information transmitted is distributed in an open access environment. As each vehicle is not a source of all messages, most of the communication depends on the information received from other vehicles. To protect VANET from malicious action, each vehicle must be able to evaluate, decide and react locally on the information received from other vehicles. Therefore, message verification is more challenging in VANET because of the security and privacy concerns of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.
Abstract: The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.
Abstract: The influence of microwave drying methods on the nutritional and anti-nutritional composition and physical characteristics of banana peels was investigated. Banana peels were assessed for physical properties such as yield, pH value, bulk density, water holding capacity (WHC) and oil holding capacity (OHC). The results showed that, the yield of banana peels and pH value was significantly (P < 0.05) decreased by microwave drying (11.20% and pH 5.08, respectively) compared with control. Bulk density was increased by microwave drying and recorded 62.03 g/100 ml. The banana peels flour demonstrated that the highest WHC was 8.65 g water/g dry sample and OHC was 6.73 g oil/g dry sample compared to control. The results observed a significant decrease (P < 0.05) in moisture, fiber and total carbohydrates content of banana peels; whereas, the rates of ash, protein and fat content were increased after drying by microwave compared with control. The lignin content of banana peels was significantly increased (P < 0.05) by microwave drying and the recorded value was 8.31% dw. The results also revealed that the ascorbic acid content was significantly decreased by microwave drying and recorded 18.32 mg/100 g dw vis. 23.51 mg/100 g dw for control. With regarding the anti-nutrients, phytates, alkaloids, oxalates and hydrogen cyanides levels in banana peels, it was in the threshold value mentioned as safety restrict. These results demonstrated that the levels of phytates, alkaloids, oxalates and hydrogen cyanides were decreased by microwave drying methods which recorded 4.07%, 5.45%, 0.85% and 32.15%, respectively.
Abstract: In recent years, object detection has gained much
attention and very encouraging research area in the field of computer
vision. The robust object boundaries detection in an image is
demanded in numerous applications of human computer interaction
and automated surveillance systems. Many methods and approaches
have been developed for automatic object detection in various fields,
such as automotive, quality control management and environmental
services. Inappropriately, to the best of our knowledge, object
detection under illumination with shadow consideration has not
been well solved yet. Furthermore, this problem is also one of
the major hurdles to keeping an object detection method from the
practical applications. This paper presents an approach to automatic
object detection in images under non-standardized environmental
conditions. A key challenge is how to detect the object, particularly
under uneven illumination conditions. Image capturing conditions
the algorithms need to consider a variety of possible environmental
factors as the colour information, lightening and shadows varies
from image to image. Existing methods mostly failed to produce the
appropriate result due to variation in colour information, lightening
effects, threshold specifications, histogram dependencies and colour
ranges. To overcome these limitations we propose an object detection
algorithm, with pre-processing methods, to reduce the interference
caused by shadow and illumination effects without fixed parameters.
We use the Y CrCb colour model without any specific colour
ranges and predefined threshold values. The segmented object regions
are further classified using morphological operations (Erosion and
Dilation) and contours. Proposed approach applied on a large image
data set acquired under various environmental conditions for wood
stack detection. Experiments show the promising result of the
proposed approach in comparison with existing methods.
Abstract: In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object’s motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values.
Abstract: Conceiving and developing routing protocols for
wireless sensor networks requires considerations on constraints such
as network lifetime and energy consumption. In this paper, we propose
a hybrid hierarchical routing protocol named HHRP combining both
clustering mechanism and multipath optimization taking into account
residual energy and RSSI measures. HHRP consists of classifying
dynamically nodes into clusters where coordinators nodes with extra
privileges are able to manipulate messages, aggregate data and ensure
transmission between nodes according to TDMA and CDMA
schedules. The reconfiguration of the network is carried out
dynamically based on a threshold value which is associated with the
number of nodes belonging to the smallest cluster. To show the
effectiveness of the proposed approach HHRP, a comparative study
with LEACH protocol is illustrated in simulations.
Abstract: This paper presents a new automatic vehicle detection
method from very high resolution aerial images to measure traffic
density. The proposed method starts by extracting road regions from
image using road vector data. Then, the road image is divided into
equal sections considering resolution of the images. Gradient vectors
of the road image are computed from edge map of the corresponding
image. Gradient vectors on the each boundary of the sections are
divided where the gradient vectors significantly change their
directions. Finally, number of vehicles in each section is carried out
by calculating the standard deviation of the gradient vectors in each
group and accepting the group as vehicle that has standard deviation
above predefined threshold value. The proposed method was tested in
four very high resolution aerial images acquired from Istanbul,
Turkey which illustrate roads and vehicles with diverse
characteristics. The results show the reliability of the proposed
method in detecting vehicles by producing 86% overall F1 accuracy
value.
Abstract: This paper presents a wireless sensor network protocol for a car parking monitoring system. A wireless sensor network for the purpose is composed of multiple sensor nodes, a sink node, a gateway, and a server. Each of the sensor nodes is equipped with a 3-axis AMR sensor and deployed in the center of a parking space. The sensor node reads its sensor values periodically and transmits the data to the sink node if the current and immediate past sensor values show a difference exceeding a threshold value. The operations of the sink and sensor nodes are described in detail along with flow diagrams. The protocol allows a low-duty cycle operation of the sensor nodes and a flexible adjustment of the threshold value used by the sensor nodes.
Abstract: In this paper, a delayed plankton-nutrient interaction model consisting of phytoplankton, zooplankton and dissolved nutrient is considered. It is assumed that some species of phytoplankton releases toxin (known as toxin producing phytoplankton (TPP)) which is harmful for zooplankton growth and this toxin releasing process follows a discrete time variation. Using delay as bifurcation parameter, the stability of interior equilibrium point is investigated and it is shown that time delay can destabilize the otherwise stable non-zero equilibrium state by inducing Hopf-bifurcation when it crosses a certain threshold value. Explicit results are derived for stability and direction of the bifurcating periodic solution by using normal form theory and center manifold arguments. Finally, outcomes of the system are validated through numerical simulations.
Abstract: This paper presents a car parking monitoring system using wireless sensor networks. Multiple sensor nodes and a sink node, a gateway, and a server constitute a wireless network for monitoring a parking lot. Each of the sensor nodes is equipped with a 3-axis AMR sensor and deployed in the center of a parking space. Each sensor node reads its sensor values periodically and transmits the data to the sink node if the current and immediate past sensor values show a difference exceeding a threshold value. The sensor nodes and sink node use the 448 MHz band for wireless communication. Since RF transmission only occurs when sensor values show abrupt changes, the number of RF transmission operations is reduced and battery power can be conserved. The data from the sensor nodes reach the server via the sink node and gateway. The server determines which parking spaces are taken by cars based upon the received sensor data and reference values. The reference values are average sensor values measured by each sensor node when the corresponding parking spot is not occupied by a vehicle. Because the decision making is done by the server, the computational burden of the sensor node is relieved, which helps reduce the duty cycle of the sensor node.
Abstract: The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.
Abstract: Wireless Sensor Networks consist of inexpensive, low power sensor nodes deployed to monitor the environment and collect
data. Gathering information in an energy efficient manner is a critical aspect to prolong the network lifetime. Clustering algorithms have an advantage of enhancing the network lifetime. Current clustering algorithms usually focus on global re-clustering and local re-clustering separately. This paper, proposed a combination of those two reclustering methods to reduce the energy consumption of the network. Furthermore, the proposed algorithm can apply to homogeneous as well as heterogeneous wireless sensor networks. In addition, the cluster head rotation happens, only when its energy drops below a dynamic threshold value computed by the algorithm. The simulation result shows that the proposed algorithm prolong the network lifetime compared to existing algorithms.
Abstract: Dynamic bandwidth allocation in EPONs can be
generally separated into inter-ONU scheduling and intra-ONU scheduling. In our previous work, the active intra-ONU scheduling
(AS) utilizes multiple queue reports (QRs) in each report message to cooperate with the inter-ONU scheduling and makes the granted
bandwidth fully utilized without leaving unused slot remainder (USR).
This scheme successfully solves the USR problem originating from the
inseparability of Ethernet frame. However, without proper setting of
threshold value in AS, the number of QRs constrained by the IEEE
802.3ah standard is not enough, especially in the unbalanced traffic
environment. This limitation may be solved by enlarging the threshold
value. The large threshold implies the large gap between the adjacent QRs, thus resulting in the large difference between the best granted bandwidth and the real granted bandwidth. In this paper, we integrate
AS with a cooperative prediction mechanism and distribute multiple
QRs to reduce the penalty brought by the prediction error.
Furthermore, to improve the QoS and save the usage of queue reports,
the highest priority (EF) traffic which comes during the waiting time is
granted automatically by OLT and is not considered in the requested
bandwidth of ONU. The simulation results show that the proposed
scheme has better performance metrics in terms of bandwidth
utilization and average delay for different classes of packets.
Abstract: In this paper, we consider the analysis of the
acquisition process for a hybrid double-dwell system with antenna
diversity for DS-CDMA (direct sequence-code division multiple
access) using an adaptive threshold. Acquisition systems with a fixed
threshold value are unable to adapt to fast varying mobile
communications environments and may result in a high false alarm
rate, and/or low detection probability. Therefore, we propose an
adaptively varying threshold scheme through the use of a cellaveraging
constant false alarm rate (CA-CFAR) algorithm, which is
well known in the field of radar detection. We derive exact
expressions for the probabilities of detection and false alarm in
Rayleigh fading channels. The mean acquisition time of the system
under consideration is also derived. The performance of the system is
analyzed and compared to that of a hybrid single dwell system.
Abstract: In the age of global communications, heterogeneous
networks are seen to be the best choice of strategy to ensure continuous and uninterruptible services. This will allow mobile
terminal to stay in connection even they are migrating into different segment coverage through the handoff process. With the increase of
teletraffic demands in mobile cellular system, hierarchical cellular systems have been adopted extensively for more efficient channel
utilization and better QoS (Quality of Service). This paper presents a
bidirectional call overflow scheme between two layers of microcells and macrocells, where handoffs are decided by the velocity of mobile
making the call. To ensure that handoff calls are given higher priorities, it is assumed that guard channels are assigned in both
macrocells and microcells. A hysteresis value introduced in mobile velocity is used to allow mobile roam in the same cell if its velocity
changes back within the set threshold values. By doing this the number of handoffs is reduced thereby reducing the processing overhead and enhancing the quality of service to the end user.
Abstract: In this paper optimal capacitor placement problem has
been formulated in a restructured distribution network. In this
scenario the distribution network operator can consider reactive
energy also as a service that can be sold to transmission system. Thus
search for optimal location, size and number of capacitor banks with
the objective of loss reduction, maximum income from selling
reactive energy to transmission system and return on investment for
capacitors, has been performed. Results is influenced with economic
value of reactive energy, therefore problem has been solved for
various amounts of it. The implemented optimization technique is
genetic algorithm. For any value of reactive power economic value,
when reverse of investment index increase and change from zero or
negative values to positive values, the threshold value of selling
reactive power has been obtained. This increasing price of economic
parameter is reasonable until the network losses is less than loss
before compensation.
Abstract: The cellular network is one of the emerging areas of
communication, in which the mobile nodes act as member for one
base station. The cluster based communication is now an emerging
area of wireless cellular multimedia networks. The cluster renders
fast communication and also a convenient way to work with
connectivity. In our scheme we have proposed an optimization
technique for the fuzzy cluster nodes, by categorizing the group
members into three categories like long refreshable member, medium
refreshable member and short refreshable member. By considering
long refreshable nodes as static nodes, we compute the new
membership values for the other nodes in the cluster. We compare
their previous and present membership value with the threshold value
to categorize them into three different members. By which, we
optimize the nodes in the fuzzy clusters. The simulation results show
that there is reduction in the cluster computational time and
iterational time after optimization.
Abstract: In wavelet regression, choosing threshold value is a crucial issue. A too large value cuts too many coefficients resulting in over smoothing. Conversely, a too small threshold value allows many coefficients to be included in reconstruction, giving a wiggly estimate which result in under smoothing. However, the proper choice of threshold can be considered as a careful balance of these principles. This paper gives a very brief introduction to some thresholding selection methods. These methods include: Universal, Sure, Ebays, Two fold cross validation and level dependent cross validation. A simulation study on a variety of sample sizes, test functions, signal-to-noise ratios is conducted to compare their numerical performances using three different noise structures. For Gaussian noise, EBayes outperforms in all cases for all used functions while Two fold cross validation provides the best results in the case of long tail noise. For large values of signal-to-noise ratios, level dependent cross validation works well under correlated noises case. As expected, increasing both sample size and level of signal to noise ratio, increases estimation efficiency.
Abstract: Abdominal aortic aneurysms rupture (AAAs) is one of the main causes of death in the world. This is a very complex phenomenon that usually occurs “without previous warning". Currently, criteria to assess the aneurysm rupture risk (peak diameter and growth rate) can not be considered as reliable indicators. In a first approach, the main geometric parameters of aneurysms have been linked into five biomechanical factors. These are combined to obtain a dimensionless rupture risk index, RI(t), which has been validated preliminarily with a clinical case and others from literature. This quantitative indicator is easy to understand, it allows estimating the aneurysms rupture risks and it is expected to be able to identify the one in aneurysm whose peak diameter is less than the threshold value. Based on initial results, a broader study has begun with twelve patients from the Clinic Hospital of Valladolid-Spain, which are submitted to periodic follow-up examinations.